
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)







def 							A__       (    lowerCamelCase    )	->						str:
				UpperCamelCase_:				List[str]						=    r"""\w+[.]\d+"""
				UpperCamelCase_:				int						=    re.findall(lowerCamelCase  ,     lowerCamelCase    )
				for pat in pats:
								UpperCamelCase_:				Optional[int]						=    key.replace(lowerCamelCase  ,     """_""".join(pat.split("""."""    )    )    )
				return key







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Any:
				UpperCamelCase_:				Optional[int]						=    pt_tuple_key[:-1] + ("""scale""",)
				if (
				    any("""norm""" in str_ for str_ in pt_tuple_key    )
				    and (pt_tuple_key[-1] == "bias")
				    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
				    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
				):
								UpperCamelCase_:				List[str]						=    pt_tuple_key[:-1] + ("""scale""",)
								return renamed_pt_tuple_key, pt_tensor
				elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
								UpperCamelCase_:				Any						=    pt_tuple_key[:-1] + ("""scale""",)
								return renamed_pt_tuple_key, pt_tensor

				# embedding
				if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
								UpperCamelCase_:				List[str]						=    pt_tuple_key[:-1] + ("""embedding""",)
								return renamed_pt_tuple_key, pt_tensor

				# conv layer
				UpperCamelCase_:				List[Any]						=    pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
								UpperCamelCase_:				Dict						=    pt_tensor.transpose(2  ,     3  ,     1  ,     0    )
								return renamed_pt_tuple_key, pt_tensor

				# linear layer
				UpperCamelCase_:				int						=    pt_tuple_key[:-1] + ("""kernel""",)
				if pt_tuple_key[-1] == "weight":
								UpperCamelCase_:				Optional[int]						=    pt_tensor.T
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm weight
				UpperCamelCase_:				Optional[Any]						=    pt_tuple_key[:-1] + ("""weight""",)
				if pt_tuple_key[-1] == "gamma":
								return renamed_pt_tuple_key, pt_tensor

				# old PyTorch layer norm bias
				UpperCamelCase_:				List[Any]						=    pt_tuple_key[:-1] + ("""bias""",)
				if pt_tuple_key[-1] == "beta":
								return renamed_pt_tuple_key, pt_tensor

				return pt_tuple_key, pt_tensor







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=42    )	->						str:
				# Step 1: Convert pytorch tensor to numpy
				UpperCamelCase_:				Any						=    {k: v.numpy() for k, v in pt_state_dict.items()}

				# Step 2: Since the model is stateless, get random Flax params
				UpperCamelCase_:				Optional[int]						=    flax_model.init_weights(PRNGKey(lowerCamelCase    )    )

				UpperCamelCase_:				List[str]						=    flatten_dict(lowerCamelCase    )
				UpperCamelCase_:				str						=    {}

				# Need to change some parameters name to match Flax names
				for pt_key, pt_tensor in pt_state_dict.items():
								UpperCamelCase_:				List[Any]						=    rename_key(lowerCamelCase    )
								UpperCamelCase_:				Optional[Any]						=    tuple(renamed_pt_key.split("""."""    )    )

								# Correctly rename weight parameters
								UpperCamelCase_, UpperCamelCase_:				int						=    rename_key_and_reshape_tensor(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

								if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																raise ValueError(
																    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''    )

        # also add unexpected weight so that warning is thrown
								UpperCamelCase_:				str						=    jnp.asarray(lowerCamelCase    )

				return unflatten_dict(lowerCamelCase    )








from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Optional[int]						=    len(lowerCamelCase    )
				# We need to create solution object to save path.
				UpperCamelCase_:				List[str]						=    [[0 for _ in range(lowerCamelCase    )] for _ in range(lowerCamelCase    )]
				UpperCamelCase_:				Dict						=    run_maze(lowerCamelCase  ,     0  ,     0  ,     lowerCamelCase    )
				if solved:
								print("""\n""".join(str(lowerCamelCase    ) for row in solutions    )    )
				else:
								print("""No solution exists!"""    )
				return solved







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						bool:
				UpperCamelCase_:				Dict						=    len(lowerCamelCase    )
				# Final check point.
				if i == j == (size - 1):
								UpperCamelCase_:				Union[str, Any]						=    1
								return True

				UpperCamelCase_:				List[str]						=    (not i < 0) and (not j < 0)  # Check lower bounds
				UpperCamelCase_:				List[str]						=    (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								UpperCamelCase_:				Dict						=    (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												UpperCamelCase_:				Optional[Any]						=    1

												# check for directions
												if (
												    run_maze(lowerCamelCase  ,     i + 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j + 1  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     i - 1  ,     lowerCamelCase  ,     lowerCamelCase    )
												    or run_maze(lowerCamelCase  ,     lowerCamelCase  ,     j - 1  ,     lowerCamelCase    )
												):
																return True

												UpperCamelCase_:				Optional[Any]						=    0
												return False
				return False


if __name__ == "__main__":
							import doctest

							doctest.testmod()






